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Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kinds of Machine Learning technique could be effective to support early diagnosis of Multiple Sclerosis and which of them presented best performance in distinguishing Multiple Sclerosis patients from control subjects. We selected following algorithms: Random Forest, Support Vector Machine, Naïve-Bayes, K-nearest-neighbor and Artificial Neural Network. We applied the Independent Component Analysis to resting-state functional-MRI sequence to identify brain networks. We found 15 networks, from which we extracted the mean signals used into classification. We performed feature selection tasks in all algorithms to obtain the most important variables. We showed that best discriminant network between controls and early Multiple Sclerosis, was the sensori-motor I, according to early manifestation of motor/sensorial deficits in Multiple Sclerosis. Moreover, in classification performance, Random Forest and Support Vector Machine showed same 5-fold cross-validation accuracies (85.7%) using only this network, resulting to be best approaches. We believe that these findings could represent encouraging step toward the translation to clinical diagnosis and prognosis.

Background and aims: Bulbar palsy plus(BPp) is a variant of Guillain Barre syndrome (GBS), characterized by bulbar symptoms and others cranial nerves deficits or ataxia, without limbs or neck weakness, usually associated with presence of GQ1b and Gt1a antibodies. Methods: We describe a case of BPp overlapped by a sensory-motor axonal neuropathy (ASMAN). Results: A 62 years old man, without relevant medical history, was admitted to Emergency Room for dysphagia, voice reduction and paraesthesia to distal extremities started 4 days earlier, after a diarrhoeic episode. Neurological examination showed absent tendon reflexes, mild ophthalmoparesis and asymmetrical palpebral ptosis. Cerebrospinal fluid(CSF) analysis and neurophysiological study(NPS) were normal, showing only absent H reflex. Repetitive nerve stimulation at low and high frequencies was normal. Intravenous immunoglobulins 0,4 gr/kg die for 5 days were started. Subsequently he developed tetra-hyposthenia and worsening of bulbar function, complete gaze palsy and bilateral ptosis, requiring mechanical ventilation and nasogastric tube. 15 days after symptoms onset, CSF analysis showed albuminocytological dissociation and NPS showed diffuse sensory-motor axonal neuropathy. Initially no serum anti ganglioside were detected, but 15 days after GT1a, Gq1b, GD2 and GD3 IgG were positive. RT-PCR and serology for SARS-COV 2 were negative. Campylobacter search was negative. About 15 days after onset he started recovery and at 50 days he was discharged from rehabilitative ward. Conclusions: BPp is recently recognised as a rare GBS variant, such as Miller Fisher syndrome (MFS) or pharyngeal-cervical-brachial (PCB) variant. Overlap between AMSAN and the other variants was already described. This is an interesting case of BPp/ASMAN overlap syndrome.

This work evaluates the potential in diagnostic application of a new advanced neuroimaging method, which delineates the profile of tissue properties along the corticospinal tract (CST) in amyotrophic lateral sclerosis (ALS), by means of diffusion tensor imaging (DTI). Twenty-four ALS patients and twenty-four demographically matched healthy subjects were enrolled in this study. The Automated Fiber Quantification (AFQ), a tool for the automatic reconstruction of white matter tract profiles, based on a deterministic tractography algorithm to automatically identify the CST and quantify its diffusion properties, was used. At a group level, the highest non-overlapping DTI-related differences were detected in the cerebral peduncle, posterior limb of the internal capsule, and primary motor cortex. Fractional anisotropy (FA) decrease and mean diffusivity (MD) and radial diffusivity (RD) increases were detected when comparing ALS patients to controls. The machine learning approach used to assess the clinical utility of this DTI tool revealed that, by combining all DTI metrics measured along tract between the cerebral peduncle and the corona radiata, a mean 5-fold cross validation accuracy of 80% was reached in discriminating ALS from controls. Our study provides a useful new neuroimaging tool to characterize ALS-related neurodegenerative processes by means of CST profile. We demonstrated that specific microstructural changes in the upper part of the brainstem might be considered as a valid biomarker. With further validations this method has the potential to be considered a promising step toward the diagnostic utility of DTI measures in ALS. Hum Brain Mapp 38:727-739, 2017. © 2016 Wiley Periodicals, Inc.

Recently, during the pandemic infection of the novel SARS-CoV-2, some cases of Guillan-Barré Syndrome (GBS) have been reported. The aim of this work is to report the natural history of patients with GBS, both COVID and not-COVID related, hospitalized in Liguria region, during lock down period, in order to assess clinical features of both groups and possible managements pitfalls due to pandemic emergency. Fifteen GBS patients were admitted to the Hospitals of Liguria, from February 15th to May 3rd 2020, six with SARS-CoV-2 infection and nine without infection. In COVID-19 related GBS five patients presented with classical GBS and one with variant. Two patients presented neurologic symptoms during or shortly after the viral syndrome, suggesting the pattern of a para-infectious profile. Multi-organ involvement, delay in the diagnosis, incomplete work up and start of therapy, were registered in 50% of cases with a GBS-Disability scale ≥4 at follow-up evaluation. In not-COVID-19 related GBS, main problem was diagnostic delay. In three patients the first neurological observation took place after a mean of 33,6 days. Moreover, five patients went to emergency room after an average of 30 days since the onset of neurological symptoms because of fear of contagion. In conclusion, not only SARS-CoV-2 infection can cause GBS, but it can also, due to effects of pandemic on the health organization, affect the outcome of patients with not COVID-19 related GBS., Highlights • Covid-19 and GBS. • Diagnostic and therapeutic delay. • Management pitfalls.

Background and purpose: Corpus callosum (CC) is frequently involved in relapsing-remitting multiple sclerosis (RRMS). Magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) allow to study CC macrostructural and microstructural tissue integrity. Here, we applied a data-driven approach to MRI and DTI data of normal-appearing CC in RRMS subjects, and subsequently evaluated if differences in tissue integrity corresponded to different levels of physical disability and cognitive impairment. Methods: 74 RRMS patients and 20 healthy controls (HC) underwent 3 T MRI and DTI. Thickness and fractional anisotropy (FA) along midsagittal CC were extracted, and values from RRMS patients were fed to a hierarchical clustering algorithm. We then used ANOVA to test for differences in clinical and cognitive variables across the imaging-based clusters and HC. Results: We found three distinct MRI-based subgroups of RRMS patients with increasing severity of CC damage. The first subgroup showed callosal integrity similar to HC (Cluster 1); Cluster 2 had milder callosal damage; a third subgroup showed the most severe callosal damage (Cluster 3). Cluster 3 included patients with longer disease duration and worst scores in Expanded Disability Status Scale. Cognitive domains of verbal memory, executive functions and processing speed were impaired in Cluster 3 and Cluster 2 compared to Cluster 1 and HC. Conclusions: Within the same homogeneous cohort of patients, we could identify three neuroimaging RRMS clusters characterized by different involvement of normal-appearing CC. Interestingly, these corresponded to three distinct levels of clinical and cognitive disability.

The anti-CD52 monoclonal antibody alemtuzumab is a highly active treatment for multiple sclerosis (MS) causing rapid depletion of B and T lymphocytes with nadir one month after last infusion. Opportunistic Cytomegalovirus (CMV) infections have been reported in MS patients treated with this drug. We report one patient who developed a CMV reactivation with hepatic involvement three weeks after the first cycle of alemtuzumab. This patient, promptly diagnosed and treated, achieved a complete recovery with valganciclovir. The possibility of this treatable opportunistic infection should be considered by neurologists in febrile patients with hepatic markers alteration after treatment with alemtuzumab.

Background: Using Diffusion Tensor Imaging (DTI), we tested the hypothesis that cerebellar abnormalities in fractional anisotropy (FA) may be involved in cognitive dysfunctions in Relapsing-Remitting (RR)-Multiple Sclerosis (MS). Objective: The aim of our study was to investigate the microstructural integrity in MS of regions that connect in both feedforward and feedback pathways to cortical areas, i.e., Superior Cerebellar Peduncles (SCP), Middle Cerebellar Peduncles (MCP), Dentate nuclei (DN) and Thalamus (Th). Patients and methods: We studied 46 patients with RR-MS (21 with cerebellar signs and 25 without) and 23 normal subjects. All subjects underwent cognitive testing. Results: In patients with a cerebellar phenotype, cognitive performance in all considered domains was from moderately to strongly related (p